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Measuring Cortical Thickness Using An Image Domain Local Surface Model And Topology Preserving Segmentation
By: Avants, B.B.; Das, S.R.; Gee, J.C.; Grossman, M.;
2007 / IEEE / 978-1-4244-1630-1
This item was taken from the IEEE Conference ' Measuring Cortical Thickness Using An Image Domain Local Surface Model And Topology Preserving Segmentation ' We present a measure of gray matter (GM) thickness based on local surface models in the image domain. Thickness is measured by integrating GM probability maps along the white matter (WM) surface normal direction. The method is simple to implement and allows statistical tests to be performed in the gray matter volume. A novel topology preserving segmentation method is introduced that is able to accurately recover GM in deep sulci. We apply this methodology to a longitudinal study of gray matter atrophy in a patient cohort diagnosed with frontotemporal dementia (FTD) spectrum disorders. Following image-based normalization of GM thickness maps, results show significant reduction in cortical thickness in several Brodmann areas spanning temporal, parietal and frontal lobes across subjects.
Gm Thickness Maps
Cortical Thickness Measurement
Image Domain Local Surface Model
Topology Preserving Segmentation
Gray Matter Thickness
Gm Probability Maps
White Matter Surface Normal Direction
Patient Cohort Diagnosis
Frontotemporal Dementia Spectrum Disorders
Medical Image Processing
Local Surface Models